Workflow Instruction

Follow the workflow shown below. Locate the dataset, check the file type, and resolve any issues with missing files or wrong file types. Determine the fine-tuning model and fine-tuning strategy. Run the appropriate validation based on the model family. Summarize the results: is the dataset ready for fine-tuning?

Prerequisites

• The SDK environment has been verified (SDK version, region, execution role). If not done, activate the sdk-getting-started skill first.

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Workflow

1. Locate Dataset:

   • The full path may be a local file path, or an S3 URI
   • Resolve the full path to the dataset file, make sure read permissions are available, and help the user if the file is not found

2. Determine strategy and model:

   • File formatting depends on the currently selected fine-tuning strategy and fine-tuning base model.
   • If the strategy and model are already known from the conversation context (e.g., selected via the model-selection and finetuning-technique skills), use them.
   • If not available in context, activate the model-selection and/or finetuning-technique skills to determine them before proceeding.
   • Exception: If the user is validating an evaluation dataset (not a training dataset), neither model nor technique is required, the format detector can validate eval format (query/response structure) independently. Do not block on model-selection or finetuning-technique for eval dataset validation.

3. Check File Formatting: Run the tool format_detector.py to make sure the file conforms to formatting requirements.

   • Send the full path directly to the format_detector script as an argument
   • Do not send the model and strategy as arguments
   • Do not download data from S3
   • Do not make local copies of data